Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 44, Issue 12, Pages 3706-3715Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2006.881741
Keywords
geospatial object; object detection; object model
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We propose the use of texture motifs, or characteristic spatially recurrent patterns, for modeling and detecting geospatial objects. A method is proposed for learning a texture-motif model from object examples and detecting objects based on the learned model. The model is learned in a two-layered framework: the first learns the constituent texture elements of the motif and, the second, the spatial distribution of the elements. In the experimental session, we demonstrate the model training and selection methodology for objects given a set of training examples. The utility of such models for detecting the presence or absence of geospatial objects in large aerial image datasets comprising tens of thousands of image tiles is then emphasized.
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